Exploring Computer Use Agents: Features, Architectures, and Performance
Computer use agents are advanced tools designed to perform desktop and web tasks as a human would. Despite their potential, the mechanisms behind these agents remain complex. This analysis categorizes leading systems like OpenAI’s Computer Use Preview and Claude Computer Use, highlighting their learning processes, strengths, and limitations. Open Interpreter allows direct execution of code, while Simular Agent S/S3 employs a Behavior Best-of-N method to optimize task completion. The evaluation involves a comprehensive benchmark with 369 tasks across diverse applications. The study distinguishes between End-to-End (E2E) agents and Composed agents, weighing their robustness against user control. E2E agents simplify interactions but lack transparency, while Composed agents offer detailed insights but face higher error risks. Ultimately, users must navigate these trade-offs to select the best solution for their needs. Key features including runtime environment and local system access are crucial for determining an agent’s effectiveness in real-world scenarios.
